private void ButtonLoadData_Click(object sender, RoutedEventArgs e)
{
if (!UpDownLineSkip.Value.HasValue || !UpDownClassCount.Value.HasValue)
{
return;
}
Mapper = new CsvMapper
{
Normalized = true,
Separator = TextBoxColumnSeparator.Text[0],
DecimalSeparator =
CheckBoxDecimalSeparator.IsChecked != null && CheckBoxDecimalSeparator.IsChecked.Value ? '.' : ',',
SkipRows = (int)UpDownLineSkip.Value.Value
};
var openFileDialog = new OpenFileDialog
{
Filter = "csv file (*.csv)|*.csv",
Multiselect = false
};
if (openFileDialog.ShowDialog() != true)
{
return;
}
var filename = openFileDialog.FileName;
Data = Mapper.ReadDataFromFile(filename, Convert.ToInt32(UpDownClassCount.Value.Value));
DataGridHelper.SetTableData(DataGridSet, new TableData(Data));
ButtonStart.IsEnabled = true;
}
public void Test2([Values(500)] int iterations, [Values(100)] int columns)
{
var testDir = Path.GetDirectoryName(Path.GetDirectoryName(TestContext.CurrentContext.TestDirectory));
var dataPath = Path.Combine(testDir ?? throw new NullReferenceException(), "..", "Data", "data.csv");
var mapper = new CsvMapper();
var data = mapper.ReadDataFromFile(dataPath, 1);
var correlation = Correlation.GetCorrelation(data);
data = data.RemoveLeastRelevantColumn(correlation, columns);
var dataSet = data.SplitData(1, 2, 0);
var trainingData = dataSet[DataType.Training];
var testingData = dataSet[DataType.Testing];
var neuralNetwork = new NeuralNetwork.NeuralNetwork {
MinWeight = -5, MaxWeight = 5
};
var activationFunction = new SigmoidFunction(-1);
neuralNetwork.ActivationFunction = activationFunction;
neuralNetwork.AddLayer(new Layer(10, columns));
neuralNetwork.AddLayer(new Layer(1, 10));
var genetic = new GeneticAlgorithm.GeneticAlgorithm(100, neuralNetwork.GetConnectionCount())
{
Selection = new TournamentSelection(100, 10),
Crossover = new TwoPointCrossover(0.65, 100),
Mutation = new FlipBitMutation(0.05)
};
var keepLooping = true;
for (var it = 0; it < iterations && keepLooping; it++)
{
foreach (var chromosome in genetic.Population)
{
neuralNetwork.SetWeights(chromosome.Genome);
Parallel.For(0, trainingData.Attributes.Count, i =>
{
var res = neuralNetwork.GetResult(trainingData.Attributes[i]);
lock (chromosome)
{
chromosome.Fitness += Math.Abs(res[0] - trainingData.ObjectClass[i]);
}
});
}
keepLooping = !genetic.Population.Any(chromosome => chromosome.Fitness < 5.0);
Console.WriteLine($@"iteration: {it} | " +
$@"best: {genetic.Population.MinBy(chromosome => chromosome.Fitness).First().Fitness}");
if (keepLooping && it < iterations - 1)
{
genetic.NextGeneration();
}
}
var best = genetic.Population.MinBy(chromosome => chromosome.Fitness).First();
neuralNetwork.SetWeights(best.Genome);
var correct = testingData.Attributes
.AsParallel()
.Where((t, i) => Math.Abs(neuralNetwork.GetResult(t)[0] - testingData.ObjectClass[i]) < 0.3)
.Count();
Console.WriteLine(
$@"Population: {100}, iterations: {iterations} - fitness: {best.Fitness:0.0} " +
$@"| correctly recognized: {correct}/{testingData.Attributes.Count}");
}
